Systems and methods for preprocessing target data and generating predictions using a machine learning model

Abstract

In some embodiments, a machine learning model may be accessed and used to generate a likelihood score related to a condition. In some embodiments, pre-computed vectors may be derived from a training dataset used to build the machine learning model, and the pre-computed vectors may be used to generate processed data from target data derived from a target sample. The machine learning model may then be used on the processed data to generate the likelihood score related to the condition. As an example, subsets of the training dataset may be randomly selected, and the pre-computed vectors may be derived from the randomly-selected subsets of the training dataset. The pre-computed vectors may be applied to the target data to generate the processed data. In one use case, for example, the target data may be normalized using the pre-computed vectors.

What is claimed is: 
     
         1 . A system for facilitating cancer-related prediction accuracy using a trained model, the system comprising:
 one or more processors and non-transitory machine-readable media storing instructions that, when executed by the one or more processors, cause operations comprising:
 accessing a random forest machine learning model comprising features that are derived from a training dataset and selected for the random forest machine learning model; 
 obtaining target data derived from a target sample; 
 normalizing, using prestored model parameters derived from randomly-selected subsets of the training dataset, the target data to generate processed data; and 
 after generating the processed data using the prestored model parameters, generating, using the random forest machine learning model on the processed data, a likelihood score related to disease occurrence by inputting the processed data into nodes of the random forest machine learning model. 
   
     
     
         2 . The system of  claim 1 , wherein the likelihood score related to disease occurrence is a likelihood score related to cancer occurrence. 
     
     
         3 . A method, the method comprising:
 accessing a machine learning model comprising features that are derived from a training dataset and selected for the machine learning model;   obtaining target data derived from a target sample;   normalizing, using prestored model parameters derived from randomly-selected subsets of the training dataset, the target data to generate processed data; and   after generating the processed data using the prestored model parameters, generating, using the machine learning model on the processed data, a likelihood score related to disease occurrence by inputting the processed data into nodes of the machine learning model.   
     
     
         4 . The method of  claim 3 , wherein the likelihood score related to disease occurrence is a likelihood score related to cancer occurrence. 
     
     
         5 . The method of  claim 3 , wherein generating the likelihood score related to the disease occurrence comprises:
 generating, using the machine learning model on the processed data and without requiring renormalization of the entirety of the training dataset, the likelihood score related to disease occurrence by inputting the processed data into nodes of the machine learning model.   
     
     
         6 . The method of  claim 3 , wherein the nodes and the prestored model parameters are both derived from the training dataset. 
     
     
         7 . The method of  claim 3 , wherein the features have a non-zero co-efficient satisfying a predetermined threshold in multiple bootstrapping processes performed on different subsets of the training dataset. 
     
     
         8 . The method of  claim 3 , further comprising:
 randomly selecting a plurality of subsets of the training dataset for the randomly-selected subsets.   
     
     
         9 . The method of  claim 3 , wherein:
 obtaining the target data comprises obtaining one or more expression patterns derived from the target sample;   normalizing the target data comprises normalizing the one or more expression patterns to generate one or more processed expression patterns; and   generating the likelihood score related to diseases comprises generating, using the machine learning model on the one or more processed expression patterns.   
     
     
         10 . The method of  claim 3 , wherein generating the processed data comprises removing cross-hybridizing probes from the target data. 
     
     
         11 . The method of  claim 3 , wherein generating the likelihood score related to disease comprises generating, using the machine learning model on the processed data, a score indicating a probability of a stage of a plurality of stages of a cancer occurrence. 
     
     
         12 . The method of  claim 3 , wherein generating the likelihood score related to disease comprises generating, using the machine learning model on the processed data, a score indicating a probability of recurrence of a cancer occurrence. 
     
     
         13 . One or more non-transitory machine-readable media storing instructions, that when executed by one or more processing devices, cause operations comprising:
 accessing a machine learning model comprising features that are selected for the machine learning model and derived from a training dataset;   obtaining target data derived from a target sample;   generating, using prestored model parameters derived from randomly-selected subsets of the training dataset, processed data from the target data; and   after generating the processed data using the prestored model parameters, generating, using the machine learning model on the processed data, a likelihood value related to disease by inputting the processed data into nodes of the machine learning model.   
     
     
         14 . The one or more non-transitory machine-readable media of  claim 13 , wherein the likelihood value related to disease is a likelihood score related to cancer occurrence. 
     
     
         15 . The one or more non-transitory machine-readable media of  claim 13 , wherein the target data is derived via an array on which probes specific to targets are attached. 
     
     
         16 . The one or more non-transitory machine-readable media of  claim 13 , wherein each feature of the features of the machine learning model has a non-zero co-efficient greater than a predetermined threshold in multiple bootstrapping processes performed on different subsets of the training dataset. 
     
     
         17 . The one or more non-transitory machine-readable media of  claim 13 , wherein generating the likelihood value comprises:
 generating, using the machine learning model on the processed data and without requiring renormalization of the entirety of the training dataset, the likelihood value related to disease by inputting the processed data into nodes of the machine learning model.   
     
     
         18 . The one or more non-transitory machine-readable media of  claim 13 , wherein generating the processed data comprises removing cross-hybridizing probes from the target data. 
     
     
         19 . The one or more non-transitory machine-readable media of  claim 13 , wherein generating the likelihood value comprises generating, using the machine learning model on the processed data, a score indicating a probability a stage of a plurality of stages of a cancer occurrence. 
     
     
         20 . The one or more non-transitory machine-readable media of  claim 13 , wherein generating the likelihood value comprises generating, using the machine learning model on the processed data, a score indicating a probability of recurrence of a cancer occurrence.